Last modified: 05 Aug 2021 13:04
This course will deliver the most sophisticated Machine Learning methodologies and algorithms which would be illustrated across a wide range of applications including but not limited to images, videos, health, time series data, language processing, etc. This course provides students with the Machine Learning principles for continuing learning and working in the area of Data Science and Artificial Intelligence.
| Study Type | Postgraduate | Level | 5 |
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| Session | First Sub Session | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
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This course will present advanced Machine Learning principles with applications. Practical will help students to understand principles applying to real-world applications using cutting-edge tools and libraries. The lectures will cover supervised learning, unsupervised learning, and as well as reinforcement learning. This will include regression and classification models, stochastic gradient descent algorithms, automatic differentiation, deep neural networks, model assessment and selection, model evaluation, adversarial models, reinforcement learning, unsupervised learning models.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 50 | |
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| Assessment Weeks | Feedback Weeks | |||
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| Knowledge Level | Thinking Skill | Outcome |
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| Assessment Type | Summative | Weighting | 50 | |
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| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
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There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
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| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
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| Knowledge Level | Thinking Skill | Outcome |
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| Factual | Remember | ILO’s for this course are available in the course guide. |
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